Understanding and processing information contained in natural language queries expressing user intent is a major challenge in document selection. User queries in the form of natural language are usually fuzzy and implicit, which makes it hard to be processed by existing information retrieval systems, often requiring multiple user interaction for further clarification. Furthermore, in order to return a document in response to a query, the query and the proposed document need to be scored, with the best scored proposed document being provided to the user who entered the query. Previous deep learning based scoring methods, such as Convolutional Deep Structured Semantic Models (“CDSSM”), allow scoring query-document pairs relatively effectively, but the scores are distance/similarity based. Similarity is based on the distance between two entities. Similarity is inversely proportional to distance. However, distance/similarity based scoring provides for limited information regarding the appropriateness of a particular document being returned for a given query. So, using distance based scoring, a scoring system may return documents that have the best score, but not necessarily documents that provide meaningful responses to a query.